Abstract: The present document discloses a method for machine-learning fairness-aware data valuation processing for supervised learning, from a training dataset comprising a plurality of data records each containing data for a training instance for said supervised learning, wherein the data for each training instance comprises one or more target variables, one or more input variables and one or more protected-attribute variables, wherein fairness is defined as minimizing a data bias present in the training set in respect of the one or more protected variables.

Abstract: In various embodiments, a process for obtaining a generated dataset with a predetermined bias for evaluating algorithmic fairness of a machine learning model includes receiving an input dataset and generating an anonymized reconstructed dataset based at least on the input dataset. The process includes introducing a predetermined bias into the generated dataset, forming an evaluation dataset based at least on the generated dataset with the predetermined bias, and outputting the evaluation dataset. In various embodiments, a process for training a generative model includes configuring a generative model and receiving training data, where the training data includes a tabular dataset. The process includes using computer processor(s) and the received training data to train the generative model, where the generative model is sampled to generate a dataset with a predetermined bias.

Abstract: In various embodiments, a process for obtaining a generated dataset with a predetermined bias for evaluating algorithmic fairness of a machine learning model includes receiving an input dataset and generating an anonymized reconstructed dataset based at least on the input dataset. The process includes introducing a predetermined bias into the generated dataset, forming an evaluation dataset based at least on the generated dataset with the predetermined bias, and outputting the evaluation dataset. In various embodiments, a process for training a generative model includes configuring a generative model and receiving training data, where the training data includes a tabular dataset. The process includes using computer processor(s) and the received training data to train the generative model, where the generative model is sampled to generate a dataset with a predetermined bias.